CN111275235A

CN111275235A - System and method for optimizing a manufacturing process based on inspection of a component

Info

Publication number: CN111275235A
Application number: CN201911175329.0A
Authority: CN
Inventors: 埃里克·约翰·鲁杰罗; 詹姆斯·托尔曼
Original assignee: General Electric Co
Current assignee: General Electric Co
Priority date: 2018-12-04
Filing date: 2019-11-26
Publication date: 2020-06-12
Anticipated expiration: 2039-11-26
Also published as: US11280751B2; EP3683638A1; CN111275235B; US20200173943A1

Abstract

A system for performing a manufacturing process and method of using the same are provided. For example, a method may include performing, by a system configured to drive a manufacturing process, a set of manufacturing functions based on a digital model of a first part. The method may include obtaining, by the system, performance data associated with the second portion from a field scoring system. The method may further include building a digital model based on the performance data associated with the second portion. The method may further comprise: generating a prediction representative of the performance of the first component based on the digital model; and generating a set of manufacturing functions based on the digital model and the predictions. The method also includes manufacturing the first component according to a set of manufacturing functions.

Description

System and method for optimizing a manufacturing process based on inspection of a component

Technical Field

The present disclosure generally relates to a system for performing a manufacturing process and a method of using the same. More particularly, the present disclosure relates to a system and method of use thereof that allows inspection of components of an asset in order to drive one or more manufacturing parameters for manufacturing similar components.

Background

In a typical industrial manufacturing process, there may be a difference between the operational performance of the manufactured part when it is at-service and the expected performance of the manufactured part. For example, and without limitation, there may be differences in the expected performance of the airfoils at the time of manufacture and the airfoil's tolerance on the engine under certain operating conditions. Thus, to ensure high quality parts, industrial manufacturing processes focus on producing parts that meet tight dimensional tolerances. However, this is only a first order optimization of the part being manufactured.

For example, potential differences between manufactured parts and their performance are particularly important in aircraft engine design and maintenance. The distribution of component robustness associated with manufacturing variations is exacerbated as aircraft engine core components are forced to operate at higher temperatures with less available cooling flow. Therefore, in the manufacture of future parts, specific performance conditions must be considered that can be monitored by field inspection (partial or total) of the part. In order to infer the quality of the entire component, partial field inspections, which may be performed more frequently, should be associated with the entire field inspection. Because typical manufacturing systems lack this functionality, typical manufacturing processes cannot be integrated.

Disclosure of Invention

Embodiments of the features herein help solve or mitigate the above-mentioned problems, as well as other problems known in the art. Embodiments or variations thereof that may be implemented in accordance with the present disclosure allow for integration of field performance measurements and component robustness into the manufacturing process. In this way, embodiments may allow a manufacturing facility to adjust a manufacturing process used for a component to a functional parameter or performance metric, rather than merely optimizing the manufacturing process to produce a part having predetermined tolerances for one or more physical parameters, as is conventionally done.

For example, and without limitation, in one embodiment, a part may be manufactured based on a data driven model relating to the performance of the part and/or the performance of the asset in which the part is to be used. This is in contrast to conventional manufacturing processes which focus only on producing parts having geometric features that meet predetermined tolerances. For example, as another non-limiting example, embodiments may allow for the optimization of the manufacture of components in the hot gas path of an engine based on the thermal performance of the engine, rather than based solely on the tolerance dimensions of the components.

One example embodiment includes a method for performing a manufacturing process. The method includes performing, by a system configured to drive a manufacturing process, a set of manufacturing functions based on a digital model of a first part. The method includes obtaining, by the system, performance data relating to a second portion similar to the first portion from a field scoring system. The method further includes building a digital model based on the performance data associated with the second portion. The method further includes generating a prediction representative of the performance of the first part based on the digital model and generating a set of manufacturing functions based on the digital model and the prediction. The method also includes manufacturing the first component according to a set of manufacturing functions.

Another example embodiment provides a system for performing a manufacturing process to manufacture a first part. The system includes a processor and a memory including instructions that, when executed by the processor, cause the processor to perform certain operations. The operations may include performing a set of manufacturing functions for manufacturing the first part. The operations may also include obtaining performance data associated with the second part from the field scoring system, and constructing the digital model based on the performance data associated with the second part. The operations may further include generating predictive data indicative of performance of the first part based on the digital model, and generating a set of manufacturing functions based on the digital model and the predictive data. The operations may further include manufacturing the first part according to a set of manufacturing functions.

Additional features, modes of operation, advantages, and other aspects of various embodiments are described below with reference to the accompanying drawings. Note that this disclosure is not limited to the particular embodiments described herein. These embodiments are presented for illustrative purposes only. Other embodiments or modifications to the disclosed embodiments will be apparent to those skilled in the relevant art based on the teachings provided.

Drawings

The illustrative embodiments may take form in various components and arrangements of components. Illustrative embodiments are shown in the drawings, in which like reference numerals may indicate corresponding or similar parts throughout the several views. The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the disclosure. The novel aspects of the present disclosure will become apparent to those of ordinary skill in the relevant art in view of the drawings that can be described below.

Fig. 1 illustrates a process according to an embodiment.

Fig. 2 illustrates a method according to an embodiment.

Fig. 3 shows a system according to an embodiment.

Detailed Description

Although the illustrative embodiments are described herein with respect to particular applications, it should be understood that the present disclosure is not limited thereto. Those skilled in the art and guided by the teachings herein provided will appreciate that the present disclosure will have significant utility in other applications, modifications, and embodiments, as well as in other fields, which are within the scope of the present invention.

As previously mentioned, typical component inspection methods focus on physical parameters. For example, and without limitation, gas turbine hardware (e.g., blades, nozzles, shrouds, liners, etc.) is typically inspected using point measurements for tolerance dimensions. At the engine level, all components can be expected to operate properly for their expected life cycles, provided that the resulting characteristics fall within certain tolerances. In practice, this is not correct. For example, a turbine blade package typically only displays 1 or 2 bad blades (beyond the limit of available) out of the total package size of 60 blades. These poor outliers are located at the lower end of the part robustness profile, although within the overall allowable tolerance of the part.

Embodiments of the features herein allow for optimization of manufacturing capabilities at the process level. For example, but not limiting of, by way of example, with knowledge of the desired quality and monitoring of the output of the drilling process, it becomes possible to discern quality deviations of the manufactured part, and manufacturing process deviations of the drill hole can be identified to improve its quality.

Furthermore, the embodiments described herein include dedicated hardware, software, and combinations thereof, which are transferred from the point measurement paradigm, which generally focuses on purely geometric feature details, to the field functional inspection paradigm. For example, from a thermal perspective, the thermal robustness of the hot gas path of a component may be driven by one or more parameters. These parameters may be: 1) mass of film cooling arrangement on the outer surface; 2) the quality of the thermal or environmental barrier coating thickness distribution across the part surface; 3) the quality of the internal heat transfer coefficient within the internal channel (for serpentine cooled parts).

These qualities collectively represent the ability of the component to perform one of its intended functions: that is, the operating temperature of the component is kept below a certain threshold requirement. Furthermore, the degree to which an individual component possesses these qualities is not necessarily directly related to the dimensional measurements associated with defining a particular geometry (associated with that particular assembly). Therefore, it is necessary to measure the mass/function directly on the component to ensure that the component functions properly.

While current inspection techniques focus on obtaining geometric data from parts, these embodiments are associated with direct and functional measurement of part capabilities, and thus they allow for the production of parts that are customized to achieve a predetermined thermal robustness. According to one embodiment, the production of such parts is based on integrated field inspection data at the process level. Such data may be collected from various inspection techniques (in whole, in part, or combinations thereof) associated with the part, such as, but not limited to, pressure sensitive paint applied to the part, blue light inspection, white light inspection, and infrared-based inspection techniques. In some embodiments, the component may include a sleeve or jacket having a pressure sensitive coating on a surface thereof; in these embodiments, the pressure sensitive coating does not contact the part.

This approach is advantageous because in the above example, the component parameters of interest are three thermal parameters from an engine operating perspective. In this way, embodiments help focus the manufacturing process on the thermal or cooling performance of the component, rather than only on its geometric features.

In other words, in one embodiment, the process used to produce the components in the plant will actually be tuned to achieve a certain minimum thermal robustness, and field inspection data from one or more of the above sources may be used in conjunction to define a minimum thermal performance criterion for the entire assembly.

In one example use case, embodiments of the features herein may be used for hot gas path inspection in a turbine. Embodiments replace the geometry-centric inspection with field inspection techniques that interrogate the field and functional properties of the part. In this case, the manufacturing parameters can be fine-tuned to meet specified minimum robustness parameter requirements (in this case, thermal performance primarily) to meet expected component service life. Thus, embodiments provide novel systems and methods for integrating inspection technology on a manufacturing plant or service shop.

Thus, embodiments provide several advantages over current inspection techniques that focus on obtaining geometric data from a part. Several example embodiments are described below; although the described methods and systems are discussed in the context of aircraft components, those of ordinary skill in the art will readily appreciate that they may be applied in other contexts, i.e., in other industries, without departing from the present disclosure.

Fig. 1 shows a process 100 according to an example embodiment. The process 100 may be a process associated with a lifecycle and/or a general manufacturing cycle of a component. Although process 100 is described in the context of an aircraft or jet engine part, it may extend to a manufacturing process, or generally to the lifecycle of any manufactured component. The process 100 includes a module 102 that is a product environment scope. In other words, the module 102 may be a database that stores information about instances of the same product that are used in the field.

For example, the module 102 may include information regarding reliability or failure when multiple turbine blades are commissioned in a group of engines (i.e., in two or more engines, or generally on two or more aircraft). Module 102 may be configured to organize or present a product environment spectrum (product environment spectrum) that classifies all products of interest in a predetermined order according to requests from devices communicatively coupled thereto.

For example, products may be classified from most stable (102a) to nominal/optimal fuel combustion performance (102 n). Generally, one or more criteria may be used to classify the products according to the spectra. For example, in the case of turbine blades, products may be classified according to their thermal properties, which may be measured using one or more in-situ inspection methods, in whole or in part, or a combination thereof.

One or more of these measurements may then be provided to an analysis/analysis module to determine an overall "score" for that particular portion. In some cases, the analysis module may be based on physics-based modeling (e.g., finite element models), data-based modeling (i.e., mapping comparisons with previous knowledge about how parts with similar signals were performed in the field), machine learning/artificial intelligence models, or other methods of creating analysis modules.

The product environment spectrum may be driven by constraints from the customer, which may be collected and functionalized (i.e., placed in the form of computer instructions) in module 104. Similarly, the product environment spectrum may be driven by business constraints, which may be functionalized in module 106. These constraints (for both modules 104 and 106) may be updated as the manufacturing process is updated as various information sources are updated, as will be described further below.

Customer constraints of module 104 may also drive engineering functions of module 108, which in turn drive manufacturing decisions, as functionalized in module 112. Once the engineering decisions are functionalized, they can be used to build digital threads configured for design; this is accomplished by analyzing the creation engine module 118.

In the exemplary embodiment, the analysis is designed/created/adapted/changed/modeled in the analysis creation engine module 118. Generally, the analysis creation engine module 118 may collect information from one or more sources. For example, one or more sources may include engineering modules 108 in the form of physics-based design and simulation models. One or more sources may include field experience modules, such as module 104 and/or module 111, in the form of data associated with past product usage. One or more sources may include, part by part, previous inspection data acquired under module 114 that is directly connected with field experience data (e.g., to modules 104 and 111), part by part.

The data associated with the part at module 114 and the data associated with the same part from module 104 are linked together in a digital format for use by the analysis creation engine module 118. Further, in the exemplary embodiment, analysis-creating engine module 118 may use machine learning and/or artificial intelligence to create a surrogate model that is trained from data based on the physics-based design results, the simulation model, and the field experience module. In another embodiment, the analysis creation engine module 118 correlates the previous inspection data from module 114 with the field experience data from module 111 or module 104 and creates a regression, section by section, that is used to predict future field experience based on the future inspection data from module 114. The surrogate model for scoring the analysis module 116 is where the analytical calculations are applied to the inspection data from module 114 to create a score (102a-102n) for that particular section.

Fig. 2 illustrates an exemplary method 200 that may be performed by a manufacturing system performing process 100, according to an embodiment. The method 200 begins at step 202. Performance data for the manufactured part from known manufacturing processes/practices is functionalized by a variety of inspection techniques (step 204). These data may be generated from one or more field inspection modules (

steps

206, 208, and 210). In each of these steps, data relating to the internal thermal coefficient of the part, the film coverage quality of the part, and the full field TBC coating thickness distribution of the part, for example, may be transmitted to a subsystem module that generates the effective thermal performance of the part (step 212).

In particular, the effective thermal performance may be determined by scoring analysis module 116. In one embodiment, the determination may include comparing the estimated thermal efficiency to all other components, and a score (102a-102n) may be assigned to the manufactured part based on the comparison. The estimated thermal efficiency performance is then used to create a digital twin (step 214) which is then used to estimate the thermal performance of the newly fabricated part at step 216 by providing a performance prediction.

Having described several exemplary methods and processes, a specific system configured to perform these processes will now be described. FIG. 3 depicts a system 300 that includes a dedicated processor 314 configured to perform tasks specific to optimizing and performing a manufacturing process. The processor 314 has particular structure imparted by instructions stored in the memory 302 and/or instructions 318 that may be retrieved by the processor 314 from storage 320. The storage 320 may be co-located with the processor 314 or may be located elsewhere and communicatively coupled to the processor 314, for example, via the communication interface 316. Further, in some embodiments, the system 300 may be part of a cloud-based computing infrastructure that provides cloud-based computing services.

System 300 may be a stand-alone programmable system or may be a programmable module located in a larger system. For example, system 300 is part of a distributed system configured to process the various modules of process 100 described above. Processor 314 may include one or more hardware and/or software components configured to acquire, decode, execute, store, analyze, distribute, evaluate, and/or classify information. Further, the processor 314 may include an input/output module (I/O module 312) that may be configured to ingest data related to a single asset or group of assets.

Processor 314 may include one or more processing devices or cores (not shown). In some embodiments, processor 314 may be a plurality of processors, each having one or more cores. Processor 314 may be configured to execute instructions retrieved from memory 302 (i.e., from one of memory area 304, memory area 306, memory area 308, and memory area 310).

Moreover, without loss of generality, storage 320 and/or memory 302 may include volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, read-only, random-access, or any type of non-transitory computer-readable computer media. The storage 320 may be configured to record data processed, recorded, or collected during operation of the processor 314.

The data may be time stamped, location stamped, cataloged, indexed or organized in various ways consistent with data storage practices. The storage 320 and/or memory 302 may include programs and/or other information that the processor 314 may use to perform tasks consistent with the tasks described herein.

As described above, for example, processor 314 may be configured by instructions from memory area 306, memory area 308, and memory area 310 to perform score checking tasks and associated analysis. Processor 314 may execute the aforementioned instructions from

memory areas

306, 308, and 310 and output a twin digital model based on field performance test data and transmit the twin digital model to the manufacturing process system for subsequent manufacturing of a new part optimized based on field conditions.

Those skilled in the relevant art will appreciate that various modifications and variations of the above-described embodiments can be configured without departing from the scope and spirit of the disclosure. It is therefore to be understood that within the scope of the appended claims, the disclosure may be practiced otherwise than as specifically described herein.

Further aspects of the invention are provided by the subject matter of the following clauses:

1. a method for optimizing a manufacturing process, the method comprising performing a set of manufacturing functions of a first part by a system configured to drive the manufacturing process, the performing comprising obtaining, by the system, performance data associated with a second part from an in situ scoring system, constructing a digital model from the performance data associated with the second part, generating a prediction representative of performance of the first part based on the digital model, generating a set of manufacturing functions based on the digital model and the prediction, and manufacturing the first part according to the set of manufacturing functions.

2. The method of any preceding claim, further comprising collecting performance data via at least one inspection device.

3. The method of any preceding item, further comprising collecting performance data via one of a pressure sensitive paint, a blue light inspection device, a white light inspection device, and an infrared-based inspection device associated with the second portion.

4. The method according to any of the preceding claims, wherein the performance data relates to a thermal performance of the second part.

5. The method according to any of the preceding claims, wherein the performance data relates to thermal performance of the cooling film.

6. The method according to any of the preceding clauses, wherein the performance data relates to a thickness profile of the thermal barrier coating or the environmental barrier coating.

7. The method according to any preceding item, wherein the performance data relates to thermal efficiency of the second part.

8. The method according to any preceding clause, wherein the second portion is a hot gas path component.

9. The method according to any preceding item, wherein the manufacturing process is not optimized solely according to the geometric features of the second part.

10. A system for performing a manufacturing process to manufacture a first part, the system comprising a processor, a memory containing instructions that, when executed by the processor, cause the processor to perform operations comprising performing a set of manufacturing functions for manufacturing the first part, performing comprising obtaining performance data relating to a second part from an in-situ scoring system, building a digital model from the performance data relating to the second part, generating prediction data representing performance of the first part based on the digital model, generating a set of manufacturing functions based on the digital model and the prediction data, and manufacturing the first part according to the set of manufacturing functions.

11. The system of any of the preceding claims, wherein the operations further comprise: performance data is collected via at least one inspection device.

12. The system of any of the preceding claims, wherein the operations further comprise collecting performance data via one of a pressure sensitive paint applied directly on the second portion, a blue light inspection device, a white light inspection device, and an infrared-based inspection device.

13. The system of any of the preceding claims, wherein the performance data relates to a thermal performance of the second portion.

14. The system of any of the preceding claims, wherein the performance data relates to thermal performance of the cooling film.

15. The system according to any of the preceding clauses, wherein the performance data relates to a thickness profile of the thermal barrier coating or the environmental barrier coating.

16. The system of any of the preceding items, wherein the performance data relates to a thermal efficiency of the second part.

17. The system of any preceding claim, wherein the second portion is a component in a hot gas path of the engine.

18. The system according to any preceding item, wherein the manufacturing process is not optimized solely according to the geometric characteristics of the second part.

19. The system of any preceding claim, wherein the manufacturing process is optimized based on performance data from a plurality of field parts.

20. The system of any preceding claim, wherein the manufacturing process is optimized in accordance with performance data relating to the aircraft engine.

Claims

1. A method for optimizing a manufacturing process, the method comprising:

performing, by a system configured to drive the manufacturing process, a set of manufacturing functions of a first part, the performing comprising:

obtaining, by the system, performance data relating to the second portion from a field scoring system;

constructing a digital model from performance data relating to the second portion;

generating a prediction representative of the performance of the first portion based on the digital model;

generating the set of manufacturing functions based on the digital model and the prediction; and

the first portion is manufactured according to the set of manufacturing functions.

2. The method of claim 1, further comprising collecting the performance data via at least one inspection device.

3. The method of claim 1, further comprising collecting the performance data via one of a pressure sensitive paint, a blue light inspection device, a white light inspection device, and an infrared-based inspection device associated with the second portion.

4. The method of claim 1, wherein the performance data relates to a thermal performance of the second portion.

5. The method of claim 1, wherein the performance data relates to thermal performance of the cooling film.

6. The method of claim 1, wherein the performance data relates to a thickness profile of a thermal barrier coating or an environmental barrier coating.

7. The method of claim 1, wherein the performance data relates to a thermal efficiency of the second portion.

8. The method of claim 1, wherein the second portion is a hot gas path component.

9. The method of claim 1, wherein the manufacturing process is not optimized solely based on geometric characteristics of the second portion.

10. A system for performing a manufacturing process to manufacture a first part, the system comprising:

a processor;

a memory containing instructions that, when executed by the processor, cause the processor to perform operations comprising:

performing a set of manufacturing functions for manufacturing a first part, the performing comprising:

obtaining performance data associated with the second portion from a field scoring system;

constructing a digital model from the performance data relating to the second portion;

generating, based on the digital model, prediction data representative of the performance of the first portion;

generating the set of manufacturing functions based on the digital model and the prediction data; and